rm(list = ls())
library(data.table)
library(plyr)
library(tidyr)
library(lfe)
library(stargazer)
library(xtable)
library(sandwich)
library(roll)
library(readxl)
library(readr)
library(zoo)
library(texreg)
library(DescTools)

m <- data.table(read_xlsx("../Data/MasterData.xlsx", skip = 1, guess_max = 1e4))
m[, age := 2021 - year]

m[, memrableperiod_ownret := memrableperiod_ownret * 100]
m[, memrableperiod_pret := memrableperiod_pret * 100]
m[, memrableperiod_aret := memrableperiod_aret * 100]
m[, bias := (memrableperiod_pret - memrableperiod_aret)]

m[, retrecall_1day_rate := retrecall_1day_rate * 100]
m[, retrecall_30day_rate := retrecall_30day_rate * 100]
m[, retrecall_1year_rate := retrecall_1year_rate * 100]
m[, retrecall_5year_rate := retrecall_5year_rate * 100]

m[, expret30day_market_rate := expret30day_market_rate * 100]
m[, expret1year_market_rate := expret1year_market_rate * 100]
m[, expret30day_self_rate := expret30day_self_rate * 100]
m[, expret1year_self_rate := expret1year_self_rate * 100]

m[, memrableperiod_begin := as.yearmon(memrableperiod_begin, "%Ym%m")]
m[, memrableperiod_end := as.yearmon(memrableperiod_end, "%Ym%m")]
m[, memrableperiod_dist := as.yearmon(2022 + 2 / 12) - (memrableperiod_begin + memrableperiod_end) / 2]

###########################################################################
# Explain expectation
###########################################################################

m[, bias := (memrableperiod_pret - memrableperiod_aret)]
m[, memrableperiod_pret_length := memrableperiod_pret * memrableperiod_length]

# free recall
sample <- m[!is.na(memrableperiod_pret) & 
    !is.na(expret30day_market_rate) & !is.na(expret1year_market_rate) & 
    !is.na(expret30day_self_rate) & !is.na(expret1year_self_rate)]

f1 <- felm(expret30day_market_rate ~ memrableperiod_pret | age + gender + education + total_wealth + total_income +
    accountcheck_freq + newscheck_freq + discussion_freq + num_wechat + type | 0 | date, sample)

f2 <- felm(expret1year_market_rate ~ memrableperiod_pret | age + gender + education + total_wealth + total_income +
    accountcheck_freq + newscheck_freq + discussion_freq + num_wechat + type | 0 | date, sample)

f3 <- felm(expret30day_self_rate ~ memrableperiod_pret | age + gender + education + total_wealth + total_income +
    accountcheck_freq + newscheck_freq + discussion_freq + num_wechat + type| 0 | date, sample)

f4 <- felm(expret1year_self_rate ~ memrableperiod_pret | age + gender + education + total_wealth + total_income +
    accountcheck_freq + newscheck_freq + discussion_freq + num_wechat + type | 0 | date, sample)

stargazer(f1, f2, f3, f4,
    align = TRUE, dep.var.labels.include = TRUE,
    covariate.labels = c("Recalled return, Free Recall"),
    omit.stat = c("LL", "ser", "F"), ord.intercepts = FALSE, no.space = TRUE,
    single.row = FALSE, column.sep.width = "0pt", digits = 2
)

# probed recall
sample <- m[!is.na(retrecall_30day_rate) & !is.na(retrecall_1year_rate) &
    !is.na(expret30day_market_rate) & !is.na(expret1year_market_rate) &
    !is.na(expret30day_self_rate) & !is.na(expret1year_self_rate)]

f1 <- felm(expret30day_market_rate ~ retrecall_30day_rate + retrecall_1year_rate | age + gender + education + total_wealth + total_income +
    accountcheck_freq + newscheck_freq + discussion_freq + num_wechat + type | 0 | date, sample)

f2 <- felm(expret1year_market_rate ~ retrecall_30day_rate + retrecall_1year_rate | age + gender + education + total_wealth + total_income +
    accountcheck_freq + newscheck_freq + discussion_freq + num_wechat + type | 0 | date, sample)

f3 <- felm(expret30day_self_rate ~ retrecall_30day_rate + retrecall_1year_rate | age + gender + education + total_wealth + total_income +
    accountcheck_freq + newscheck_freq + discussion_freq + num_wechat + type | 0 | date, sample)

f4 <- felm(expret1year_self_rate ~ retrecall_30day_rate + retrecall_1year_rate | age + gender + education + total_wealth + total_income +
    accountcheck_freq + newscheck_freq + discussion_freq + num_wechat + type | 0 | date, sample)

stargazer(f1, f2, f3, f4,
    align = TRUE, dep.var.labels.include = TRUE,
    covariate.labels = c("Recalled own return, 1M", "Recalled own return, 1Y"),
    omit.stat = c("LL", "ser", "F"), ord.intercepts = FALSE, no.space = TRUE,
    single.row = FALSE, column.sep.width = "0pt", digits = 2
)

# Table A.11: Relationship between recalls and beliefs, additional analysis
sample <- m[!is.na(memrableperiod_pret) &
     !is.na(retrecall_5year_rate) &
    !is.na(expret30day_market_rate) & !is.na(expret1year_market_rate) &
    !is.na(expret30day_self_rate) & !is.na(expret1year_self_rate)]

f1 <- felm(expret1year_market_rate ~ memrableperiod_pret + retrecall_5year_rate | age + gender + education + total_wealth + total_income +
    accountcheck_freq + newscheck_freq + discussion_freq + num_wechat + type | 0 | date, sample)

f2 <- felm(expret1year_self_rate ~ memrableperiod_pret + retrecall_5year_rate | age + gender + education + total_wealth + total_income +
    accountcheck_freq + newscheck_freq + discussion_freq + num_wechat + type | 0 | date, sample)

f3 <- felm(expret1year_market_rate ~ memrableperiod_pret + expret30day_market_rate | age + gender + education + total_wealth + total_income +
    accountcheck_freq + newscheck_freq + discussion_freq + num_wechat + type | 0 | date, sample)

f4 <- felm(expret1year_self_rate ~ memrableperiod_pret + expret30day_self_rate | age + gender + education + total_wealth + total_income +
    accountcheck_freq + newscheck_freq + discussion_freq + num_wechat + type | 0 | date, sample)

stargazer(f1, f2, f3, f4,
    align = TRUE, dep.var.labels.include = TRUE,
    covariate.labels = c("Recalled return, Free Recall", "Recalled own return, 5Y"),
    omit.stat = c("LL", "ser", "F"), ord.intercepts = FALSE, no.space = TRUE,
    single.row = FALSE, column.sep.width = "0pt", digits = 2
)
